Model of Magnetic Barkhausen Noise for Characterization of Steel Materials

用于表征钢材的巴克豪森磁噪声模型

• 批准号: RGPIN-2017-06365
• 负责人: Krause, Thomas
• 金额: $ 2.26万
• 依托单位: Royal Military College of Canada
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683974
• 关键词: Model; Magnetic; Barkhausen; Noise; Characterization

The goal of this scientific investigation is to develop portable surface magnetic Barkhausen noise (MBN) capability for non-destructive characterization of ferromagnetic steel properties. The goal includes combined measurement of residual stress, degree of plastic deformation, grain size, carbon content, crystallographic texture, magnetic anisotropy and, in the case of electrical steels, core loss. Motivation for this work is the requirement for rapid identification of steel type and evaluation of stress state within structures such as submarine and ship hulls, oil and gas pipelines, motor or transformer laminates, landing gear in aircraft and cases where structures may have undergone localized damage and/or repair. Development of tools, analysis methods and modeling, along with identification of factors that affect the measurement outcome will facilitate application of MBN analysis and advance its potential as a diagnostic tool for identification and characterization of steel materials.*******The scope of work will include characterization of a wide range of steel materials, in order to develop models of MBN generation that will allow the extraction of key material parameters for non-destructive characterization of materials. The five year project will examine Barkhausen noise emissions from a number of steel types with the goal of developing general models describing micro-magnetic Barkhausen emissions in order to enhance MBN inspection capabilities for future applications. The specific ferromagnetic steel properties to be evaluated will include characterization of magnetic properties and optimization of core loss and permeability in non-oriented electrical steels (NOES), the detection and measurement of residual elastic and/or plastic stress in structural steels, including naval vessels, with a focus on monitoring of fatigue and carbide precipitate migration, as may occur during tempering embrittlement in HY80, used for submarines, and identification/assessment of specific steel grades, such as would be useful in the oil and gas industry, where installed pipelines may be made of unknown steel material. Barkhausen noise will be examined for specific NOES steels obtained under controlled manufacturing processes, as part of a collaboration with CanmetMATERIALS, Natural Resources Canada (see attached letter indicating in-kind support of $150,000-$250,000 over 5 years). Determination of naval steel condition including presence of elastic residual stress, plastic deformation and potential for detection and characterization of tempering embrittlement will be performed in collaboration with Defence Research Development Canada (DRDC), Atlantic Research Centre (see attached letter of support). Identification of signal parameters that could be used to identify steels will be performed in collaboration with Lynann Clapham at Queen's University and CanmetMATERIALS.***

这项科学研究的目标是开发便携式表面磁巴克豪森噪声（MBN）的能力，非破坏性表征铁磁性钢的性能。目标包括残余应力、塑性变形程度、晶粒尺寸、碳含量、晶体结构、磁各向异性以及在电工钢的情况下铁损的综合测量。这项工作的动机是需要快速识别钢材类型和评估结构内的应力状态，如潜艇和船体，石油和天然气管道，电机或Transformer层压板，飞机起落架和结构可能发生局部损坏和/或维修的情况。开发工具、分析方法和建模，沿着识别影响测量结果的因素，将促进MBN分析的应用，并提高其作为钢材识别和表征的诊断工具的潜力。工作范围将包括各种钢铁材料的表征，以开发MBN生成模型，从而提取关键材料参数，用于材料的非破坏性表征。这个为期五年的项目将研究多种钢材的巴克豪森噪声排放，目的是开发描述微磁巴克豪森排放的通用模型，以提高MBN未来应用的检测能力。待评估的具体铁磁性钢性能将包括无取向电工钢（NOES）的磁性能表征和铁损及磁导率优化，结构钢（包括海军舰艇）中残余弹性和/或塑性应力的检测和测量，重点是监测HY 80回火脆化过程中可能发生的疲劳和碳化物沉淀迁移，用于潜艇，以及识别/评估特定的钢等级，例如在石油和天然气工业中是有用的，其中安装的管道可能由未知的钢材料制成。作为与加拿大自然资源部CanmetMATERIALS合作的一部分，将对在受控制造工艺下获得的特定NOES钢的巴克豪森噪声进行检查（见随附信函，表明5年内的实物支持为150，000 - 250，000美元）。海军用钢状态的确定，包括存在的弹性残余应力、塑性变形以及回火脆化检测和表征的可能性，将与加拿大国防研究发展部（DRDC）大西洋研究中心合作进行（见随附的支持信）。可用于识别钢材的信号参数的识别将与皇后大学的Lynann Clapham和CanmetMATERIALS合作进行。*